What Is an LLM? A Beginner-Friendly Guide to Large Language Models

Short answer: An LLM (Large Language Model) is an AI model trained on very large amounts of text to understand and generate human language. At its core, an LLM predicts the next piece of text (a token) based on everything that came before it. Repeating that prediction many times lets it answer questions, write, summarise, translate and generate code. Popular examples include the GPT family behind ChatGPT, Claude, Gemini and Meta’s Llama.

Key takeaways

• An LLM is a next-token prediction model trained on huge text datasets.
• Most modern LLMs are built on the Transformer architecture, introduced in the 2017 paper “Attention Is All You Need”.
• LLMs work with tokens and have a limited context window.
• They can hallucinate, producing fluent but false answers, so real applications add retrieval, tools and evaluation.
• Developers usually build on top of existing LLMs via APIs or open-source models rather than training their own.

What does “large language model” mean?

• Large: the model has a very large number of learnable parameters (often billions) and is trained on a massive amount of text.
• Language: it is designed to work with human language, and often code too.
• Model: a mathematical function learned from data that maps inputs (your prompt) to outputs (its response).

How do large language models work?

1. Text becomes tokens

LLMs don’t read words the way we do. Text is split into tokens: whole words, parts of words or punctuation. In English, one token is often roughly three-quarters of a word on average, though it varies by language and tokenizer. Each token is converted into numbers the model can process.

2. The Transformer pays “attention”

The Transformer architecture uses a mechanism called self-attention, which lets the model weigh how relevant each token is to every other token. That’s how it understands that in “The bank approved the loan,” “bank” means a financial institution, not a river bank.

3. It predicts the next token

Given the tokens so far, the model calculates probabilities for what comes next, picks one, appends it, and repeats. Settings such as temperature control how predictable or creative those choices are.

4. Training happens in stages

1. Pre-training: the model learns language patterns by predicting the next token across enormous text collections.
2. Fine-tuning / instruction tuning: it’s trained further on examples of following instructions and having helpful conversations.
3. Alignment with human feedback: techniques such as reinforcement learning from human feedback (RLHF) help make responses more helpful and safer.

Key LLM terms every beginner should know

Term	Simple meaning
Token	A small chunk of text the model processes
Context window	The maximum amount of text (in tokens) the model can consider at once, covering your prompt plus its response
Prompt	The instructions and input you give the model
Temperature	Controls randomness. Lower is more consistent, higher is more varied
Parameters	The learned numerical weights inside the model
Inference	Using a trained model to generate output
Fine-tuning	Further training a model on specific examples
Embeddings	Numerical representations of meaning, used for search and RAG
Hallucination	A confident-sounding but incorrect or invented answer

LLM vs Generative AI: what’s the difference?

Generative AI is the broad category of AI that creates new content: text, images, audio, video or code. LLMs are the type of generative AI focused on language (and often code). So every LLM is generative AI, but not all generative AI is an LLM. Image generators, for example, use different model types.

What can LLMs do?

• Answer questions and explain concepts
• Summarise long documents
• Draft emails, reports and marketing copy
• Write, explain and debug code
• Extract structured data (for example, turning invoices into JSON)
• Translate between languages
• Power chatbots, assistants and AI agents that use tools

What are the limitations of LLMs?

• Hallucinations: they can produce wrong facts confidently.
• Knowledge cutoff: they don’t know about events after their training data unless given that information.
• No access to your private data by default.
• Context limits: very long documents may not fit or may be handled less reliably.
• Bias: they can reflect biases present in training data.
• Cost and latency: large models can be expensive and slow at scale.

This is why real applications combine LLMs with RAG (Retrieval-Augmented Generation), tools and evaluation.

How do developers build LLM applications?

Most developers don’t train LLMs from scratch, which takes enormous compute and data. Instead they:

1. Call an LLM API (hosted models) or run an open-source model via Hugging Face
2. Design prompts and request structured outputs
3. Add retrieval (RAG) to ground answers in company data
4. Give the model tools (search, database, APIs) to build agents
5. Evaluate and monitor quality, cost and safety
6. Deploy as an API or web application

That full path is laid out in our Generative AI Developer Roadmap.

Open-source vs closed (API) LLMs

Factor	API / closed models	Open-weight models
Setup	Easy: call an API	More work: host and serve the model
Capability	Often the most capable frontier models	Rapidly improving, wide range of sizes
Data control	Data is sent to the provider (under their terms)	Can run fully on your own infrastructure
Cost model	Pay per token	Pay for your own compute
Customisation	Limited to what the provider offers	Full fine-tuning possible

How can I start learning LLMs?

1. Learn Python basics and how APIs work
2. Experiment with prompts and observe how temperature and instructions change outputs
3. Call an LLM API from Python and build a tiny app (for example, a summariser)
4. Learn embeddings and build a simple RAG chatbot
5. Explore Hugging Face for open-source models

Frequently asked questions

Is ChatGPT an LLM?

ChatGPT is an application built on OpenAI’s GPT family of large language models, with additional features such as conversation memory and tools.

Do LLMs actually understand language?

LLMs learn statistical patterns that let them use language very effectively. Whether that counts as “understanding” is debated. Practically, treat them as powerful but fallible tools that need verification.

Do I need to know deep learning to use LLMs?

No. To build LLM applications you mainly need Python, APIs, prompting and RAG. Deep learning knowledge helps if you want to fine-tune or research models.

What is the difference between an LLM and a chatbot?

An LLM is the underlying model. A chatbot is an application that may use an LLM, plus a user interface, memory, retrieval and business rules.

What jobs use LLM skills?

Roles such as GenAI developer, AI engineer, LLM engineer, ML engineer and increasingly data scientist and software developer roles use LLM skills.

Conclusion

An LLM is a next-token prediction model, built on the Transformer architecture and trained on vast text, that can be guided with prompts to perform a remarkable range of language tasks. Understanding tokens, context windows, hallucinations and how applications add retrieval and tools is the foundation for any GenAI career.

Want to build real LLM applications? See our Generative AI App Developer course and Artificial Intelligence course, or talk to Mehul.